شماره ركورد كنفرانس :
5048
عنوان مقاله :
Comparison between Backpropagation, Elman and Radial Basis Function (RBF) networks in modeling of Tehran refinery hydrocracking unit
Author/Authors :
Kh ،Sharifi Department of Chemical Engineering - Iran University of Science and Technology - Narmak Street - Tehran, Iran , M ،Bahmani Department of Chemistry - Applied Chemistry Group - Tarbiat Moalem University - Dr. Mofatteh Street - Tehran, Iran , M ،Shirvani Department of Chemical Engineering - Iran University of Science and Technology - Narmak Street - Tehran, Iran
كليدواژه :
Artificial neural networks , Backpropagation , Elman , RBF , Hydrocracking process
عنوان كنفرانس :
ششمين كنگره بين المللي مهندسي شيمي
چكيده لاتين :
Different Artificial Neural Networks for modeling the hydrocracking process were utilized and their abilities were
compared. The input–output data for the training and simulation phases of the networks were obtained from the Tehran
refinery ISOMAX unit. Backpropagation, Elman and Radial Basis Function (RBF) networks were used for modeling
and simulation of the hydrocracking unit. For each network model several architectures were studied and the best
parameters for each network were obtained. The trained networks predict the yields of products of the ISOMAX unit
(diesel, kerosene, light naphtha and heavy naphtha) with good accuracy. The residual error (root mean squared
difference), coefficient correlation and run time, are three criteria that have been used for selection of the best network
for modeling the hydrocracking unit.
